DNA array technologies have made it possible to monitor the expression level of a large number of genetic transcripts at any one time (see, e.g., Schena et al., 1995, Science 270:467-470; Lockhart et al., 1996, Nature Biotechnology 14:1675-1680; Blanchard et al., 1996, Nature Biotechnology 14:1649; Ashby et al., U.S. Pat. No. 5,569,588, issued Oct. 29, 1996). Of the two main formats of DNA arrays, spotted cDNA arrays are prepared by depositing PCR products of cDNA fragments with sizes ranging from about 0.6 to 2.4 kb, from full length cDNAs, ESTs, etc., onto a suitable surface (see, e.g., DeRisi et al., 1996, Nature Genetics 14:457-460; Shalon et al., 1996, Genome Res. 6:639-645; Schena et al., 1996, Proc. Natl. Acad. Sci. U.S.A. 93:10614-10619; and Duggan et al., Nature Genetics Supplement 21:10-14). Alternatively, high-density oligonucleotide arrays containing thousands of oligonucleotides complementary to defined sequences, at defined locations on a surface are synthesized in situ on the surface by, for example, photolithographic techniques (see, e.g., Fodor et al., 1991, Science 251:767-773; Pease et al., 1994, Proc. Natl. Acad. Sci. U.S.A. 91:5022-5026; Lockhart et al., 1996, Nature Biotechnology 14:1675; McGall et al., 1996, Proc. Natl. Acad. Sci. U.S.A. 93:13555-13560; U.S. Pat. Nos. 5,578,832; 5,556,752; 5,510,270; and 6,040,138). Methods for generating arrays using inkjet technology for in situ oligonucleotide synthesis are also known in the art (see, e.g., Blanchard, International Patent Publication WO 98/41531, published Sep. 24, 1998; Blanchard et al., 1996, Biosensors and Bioelectronics 11:687-690; Blanchard, 1998, in Synthetic DNA Arrays in Genetic Engineering, Vol. 20, J. K. Setlow, Ed., Plenum Press, New York at pages 111-123). Efforts to further increase the information capacity of DNA arrays range from further reducing feature size on DNA arrays so as to further increase the number of probes in a given surface area to sensitivity- and specificity-based probe design and selection aimed at reducing the number of redundant probes needed for the detection of each target nucleic acid thereby increasing the number of target nucleic acids monitored without increasing probe density (see, e.g., Friend et al., International Publication No. WO 01/05935, published Jan. 25, 2001).
By simultaneously monitoring tens of thousands of genes, DNA array technologies have allowed, inter alia, genome-wide analysis of mRNA expression in a cell or a cell type or any biological sample. Aided by sophisticated data management and analysis methodologies, the transcriptional state of a cell or cell type as well as changes of the transcriptional state in response to external perturbations, including but not limited to drug perturbations, can be characterized on the mRNA level (see, e.g., Stoughton et al., International Publication No. WO 00/39336, published Jul. 6, 2000; Friend et al., International Publication No. WO 00/24936, published May 4, 2000). Applications of such technologies include, for example, identification of genes which are up regulated or down regulated in various physiological states, particularly diseased states. Additional exemplary uses for DNA arrays include the analyses of members of signaling pathways, and the identification of targets for various drugs. See, e.g., Friend and Hartwell, International Publication No. WO 98/38329 (published Sep. 3, 1998); Stoughton, International Publication No. WO 99/66067 (published Dec. 23, 1999); Stoughton and Friend, International Publication No. WO 99/58708 (published Nov. 18, 1999); Friend and Stoughton, International Publication No. WO 99/59037 (published Nov. 18, 1999); Friend et al., U.S. Pat. No. 6,218,122 (filed on Jun. 16, 1999).
The various characteristics of this analytic method make it particularly useful for directly comparing the abundance of mRNAs present in two cell types. For example, an array of cDNAs was hybridized with a green fluor-tagged representation of mRNAs extracted from a tumorigenic melanoma cell line (UACC-903) and a red fluor-tagged representation of mRNAs was extracted from a nontumorigenic derivative of the original cell line (UACC-903+6). Monochrome images of the fluorescent intensity observed for each of the fluors were then combined by placing each image in the appropriate color channel of a red-green-blue (RGB) image. In this composite image, one can see the differential expression of genes in the two cell lines. Intense red fluorescence at a spot indicates a high level of expression of that gene in the nontumorigenic cell line, with little expression of the same gene in the tumorigenic parent. Conversely, intense green fluorescence at a spot indicates high expression of that gene in the tumorigenic line, with little expression in the nontumorigenic daughter line. When both cell lines express a gene at similar levels, the observed array spot is yellow.
In some cases, visual inspection of such results is sufficient to identify genes which show large differential expression in the two samples. A more thorough study of the changes in expression requires the ability to discern quantitatively changes in expression levels and to determine whether observed differences are the result of random variation or whether they are likely to reflect changes in the expression levels of the genes in the samples. Assuming that DNA products from two samples have an equal probability of hybridizing to the probes, the intensity measurement is a function of the quantity of the specific DNA products available within each sample. Locally (or pixelwise), the intensity measurement is also a function of the concentration of the probe molecules. On the scanning side, the fluorescent light intensity also depends on the power and wavelength of the laser, the quantum efficiency of the photomultiplier tube, and the efficiency of other electronic devices. The resolution of a scanned image is largely determined by processing requirements and acquisition speed. The scanning stage imposes a calibration requirement, though it may be relaxed later. The image analysis task is to extract the average fluorescence intensity from each probe site (e.g., a cDNA region).
The measured fluorescence intensity for each probe site comes from various sources, e.g., background, cross-hybridization, hybridization with sample 1 or sample 2. The average intensity within a probe site can be measured by the median image value on the site. This intensity serves as a measure of the total fluors emitted from the sample mRNA targets hybridized on the probe site. The median is used as the average to mitigate the effect of outlying pixel values created by noise.
Typically, in a two-color microarray gene expression experiment, the experiment sample is labeled in one dye color (Cy5, red) and the control sample is labeled in a different color (Cy3, green). The two samples are mixed and hybridized to a micro-array slide. After hybridization, the expression intensity is measured with a laser scanner of two different colors. The experiment is conducted in a biology laboratory (wet lab). To obtain the expression profile, we compute the logarithmic ratio of the two measured intensities (red and green).
There are various types of biases (errors), e.g., inter-slide bias and color bias, which may affect the accuracy of the ratio estimation. Inter-slide bias is the difference between two separated slides. The two-color technique avoids the inter-slide error by running the experiment in a single slide. But different dyes can cause difference between the two intensity measurements, so that the ratio is biased. To overcome this color bias problem, the experiment can be run twice with reversed fluorescent dye labeling from one to the other. The two expression ratios are then combined to cancel out the color bias. A method for calculating individual errors associated with each measurement made in repeated microarray experiments was also developed. The method offers an approach for minimizing the number of times a cellular constituent quantification experiment must be repeated in order to produce data that has acceptable error levels and for combining data generated in repeats of a cellular constituent quantification experiment based on rank order of up-regulation or down-regulation. See, e.g., Stoughton et al., U.S. Pat. No. 6,351,712.
U.S. Pat. No. 6,691,042 discloses methods for generating differential profiles A vs. B, i.e., differential profiles between samples having been subject to condition A and condition B, from data obtained in separately performed experimental measurements A vs. C and B vs. D. When C and D are the same, i.e., common, the methods involve determination of systematic measurement errors or biases between measurements carried out in different experimental reactions, i.e., cross-experiment errors or biases, using data measured for samples under the common condition and for removal or reduction of such cross-experiment errors. U.S. Pat. No. 6,691,042 also provides methods for generating differential profiles A vs. B from data obtained in separately performed single-channel measurements A and B.
Discussion or citation of a reference herein shall not be construed as an admission that such reference is prior art to the present invention.